Determination of protein function

ABSTRACT

For purposes of determining the function of a protein, an automated system captures images of cells, each cell located in a predetermined well. After a given cell is exposed to a protein of interest, the system measures the responses of the cell over time, evaluating a variety of cellular parameters. Analytical software within the system evaluates data generated by these measurements, at single-cell resolution. By comparing with various controls the data thus obtained, the system illuminates the function of a protein with respect to one or more disease models, independent of information regarding the structure, chemistry or underlying genomics of the protein.

FIELD OF INVENTION

The present invention relates to the field of proteomics, whichencompasses the study of the expression, modification, interactions andfunction of proteins. More specifically, this invention relates tofunctional proteomics, which focuses on how proteins function in thehuman body and how they impact human health and disease.

BACKGROUND OF THE INVENTION

Proteins are involved in every biological function. They affectbiological processes directly, such as through protein signaltransduction, and indirectly, such as by enzymes and hormones. Proteinsalso are involved in disease responses and progressions, such as theinflammatory response to an injury, and the deadly course that malignanttumors take if left unchecked. Proteins determine the shape, structure,division, growth, behavior and death of cells. Proteins are the maininstruments of molecular recognition and catalysis, participating inevery cellular process and reaction.

Proteins are made from an assortment of 20 amino acids strung togetherlike pearls on a necklace. The DNA comprising a protein's genedetermines the type and order of amino acids in a protein. The humangenome comprises approximately 35,000 genes. These genes produceapproximately 300,000 to 500,000 proteins. The specific sequence ofamino acids dictates a protein's structure, called its conformation. Theprecise chemical properties of a protein's conformation enable theprotein to perform a specific catalytic or structural function in acell. Thus, the structure of a protein is a strong determinant of itsfunction. In fact, proteins with similar or related structures oftenimply related functions.

While the nucleotide sequences of genes that make up the human genomerecently have been elucidated, the function has been determined for onlyabout 20% to 30% of the encoded proteins. Since establishing proteinfunction is a key part of any drug discovery effort, drug companies haveemployed a variety of methods to infer protein function. For example,researchers often infer protein function by comparison to homologousproteins that have established functions. One such method uses massspectrometry to define the linear sequence of amino acids that make up aprotein molecule. Computer models then are employed to compare thecomposition and conformation of a protein of interest to those of knownproteins. Based on the observed homology, the protein is assigned aputative function.

Researchers also examine protein-protein associations to inferdisease-linked function. Mass spectrometry can be used to investigateprotein-protein interactions by the isolating protein complexes andsubsequently identifying the proteins in the complexes. Yeast two-hybridsystems also have been developed to study protein interactions asdescribed, for example, in U.S. Pat. No. 6,057,101. These systemsevaluate protein-protein interactions by isolating proteins thatinteract with the protein of interest, typically by screening a cDNAlibrary.

Another method-for studying protein-protein interaction is phagedisplay. The basic process is to grow and select bacteriophages thatexpress certain antibodies or proteins at their surfaces. The resultingphages are evaluated to determine which phages bear antibodies with ahigh affinity for the selected antigen.

A variety of cell-based assays have been employed to evaluateprotein-protein interactions. Examples include, but are not limited to,in vitro cytotoxity assays, soft agar colony formation assays, in vitroanti-microbial assays and assays that detect changes in cellularmorphology of the cancer cells. Automated versions of these assays alsohave been developed. For example, see U.S. Pat. No. 6,127,133 and No.6,232,083.

Disease-linked expression profiling also is employed to infer proteinfunction. Two-dimension (2D) gel separation is an example of thismethod. The 2D gel method separates proteins in a sample by displacementin two dimensions. After isolation, the proteins are further studied orcharacterized, usually by mass spectrometry. The 2D gel method isfurther explained in patents U.S. Pat. No. 6,278,794 and No. 6,064,754.Existing 2D gel methods can identify proteins that are expresseddifferentially in diseased verses healthy tissue or cells. Identifiedproteins can then be analyzed by mass spectrometry to identify thespecific protein composition.

Protein microarrays also can be used in disease-linked expressionprofiling. Typically a multiple-well plate or slide will contain manydifferent combinations of proteins. This method can be used to studyprotein-protein interactions and protein-ligand interactions.Miniaturized assays are used to accommodate extremely low sample volumesand to enable the rapid, simultaneous processing of thousands ofproteins.

While a variety of approaches are available to infer protein function,the methods are labor intensive, costly and typically generate bothfalse positives and false negatives. Furthermore, the challenge ofdemonstrable functional relevance remains an inevitable downstream stepin the development of promising drug candidates. Moreover, since aprotein's putative function can often differ from its real function, thecurrent practice of determining functional relevance during the laterstages of development increases the cost and cycle time of drugdiscovery.

SUMMARY OF THE INVENTION

Accordingly, the present invention addresses a need for an efficient andcost-effective approach to determining the function of a protein.

The invention also addresses a need for a methodology that correlatesprotein function to aspects of a pathology, independent of informationabout the structure or molecular biology of the protein.

In meeting these and other needs, there has been provided, in accordancewith one aspect of the present invention, a protein-analysis methodcomprising (A) bringing a protein into contact with at least a firstdisease-model cell and a second disease-model cell, respectively,wherein each of the first and second cells is located in a separatewell; then (B) determining the dynamic state of each of the cells,whereby a data set is generated for each cell; and (C) analyzing thedata set for the first cell and the data set for the second cell, toobtain information about the function of the protein. In one embodiment,the data sets of step (C) address a plurality of cell parameters. Thedetermination of the dynamic state can comprise imaging each of thecells by either visible or fluorescent light, or both. In anotherembodiment, the first disease-model cell and the second disease-modelcell relate to the same disease model. In another aspect of theinvention, the method further comprises providing a plurality ofproteins, wherein step (A) comprises bringing into contact, with Nnumber of disease-model cells, a chosen protein from the plurality suchthat each of at least some of the N cells contacts a different proteinfrom the plurality, N being an integer greater than 2. The data sets ofstep (C) of such a method can address a plurality of M cell parameters,M being an integer of 1 or greater, and can be organized as an N x Marray of values. In a preferred embodiment, the cell parameters comprisetwo or more of the measured parameters enumerated in Table I. In oneaspect of the invention, more than one well receives the same proteinfrom the plurality of proteins, while in another at least one wellreceives more than one protein from the plurality.

In preferred applications of the inventive method, either the firstdisease-model cell or the second disease-model cell employs anoncogenesis disease model, a primary immune response disease model or anangiogenesis disease model.

In other aspects of the invention, step (A) comprises bringing theprotein into contact with a first plurality of first disease-model cellsand a second plurality of second disease-model cells, respectively, andwherein the information distinguishes a subpopulation of at least one ofthe first and second pluralities.

In another embodiment, the present invention provides an integral arrayof biochambers, each (i) comprising a well in which a disease-model cellis located and (ii) defining a separate, closed environment for thecell, wherein each well contains a protein and the array presents apredetermined pattern of association between wells and proteins.

The invention also provides a protein-analysis method comprising (A)disposing a first disease-model cell in a first well in a manner whereinat least one cell is individually observable; (B) disposing a seconddisease-model cell in a second well in a manner wherein at least onecell is individually observable; (C) bringing a protein into contactwith the first and second disease-model cells; (D) repeatedly observingthe first and second disease-model cells; (E) compiling data in the formof data: sets pertaining to a change in at least one of a plurality ofobservable characteristics of each of the respective first and seconddisease-model cells, prior to and subsequent to the protein beingcontacted with the first and second disease-model cells; and (F)analyzing the data sets to obtain information about the function of theprotein. In one embodiment, steps (A) through (D) are implementedrobotically within a closed environment, while in another the step ofrepeatedly observing is carried out optically, without fixation ofcells. In another embodiment, steps (A) through (F) are implementedrobotically. Observable characteristics typically employed in theclaimed method include, for example, cell movement, cell division,apoptosis, morphology, adherence and physiological function, as well asthe measured parameters enumerated in Table I. In another embodiment,the method further comprises means for selectively adding a modifyingagent in addition to the protein.

In another embodiment, the invention provides a protein-analysisapparatus comprising means for disposing a plurality of firstdisease-model cells in a first well in a manner wherein at least one ofthe first disease-model cells is individually observable; means fordisposing a plurality of second disease-model cells in a second well amanner wherein at least one of the second disease-model cells isindividually observable; means for bringing a protein into contact withat least one of the first and second disease-model cells; means forrepeatedly observing the first and second disease-model cells; means forcompiling and analyzing data in the form of data sets that pertain to achange in at least one of a plurality of observable characteristics ofeach of the respective first and second disease-model cells, prior toand subsequent to the protein being contacted with the first and seconddisease-model cells.

The invention further provides a protein-analysis method comprising (A)disposing a disease-model cell in a well in a manner wherein at leastone cell is individually observable; (B) bringing a plurality ofproteins into contact with the disease-model cell;(D) repeatedlyobserving the disease-model cell; (E) compiling data in the form of datasets pertaining to a change in at least one of a plurality of observablecharacteristics of disease-model cell, prior to and subsequent to theproteins being contacted with the disease-model cell; and (F) analyzingthe data sets to obtain information about the function of the proteins.In one embodiment, the method further comprises isolating a protein ofinterest by splitting the plurality into a smaller number of pluralitiesand repeating steps (A) through (F), using the smaller number ofpluralities for step (B). In another embodiment, the method furthercomprises isolating a protein of interest by splitting the pluralityinto individual proteins and repeating steps (A) through (F) for each ofthe proteins.

Other objects, features and advantages of the present invention willbecome apparent from the following detailed description. The detaileddescription and specific examples, while indicating preferredembodiments, are given for illustration only as various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a schematic representation of the components of a devicefor carrying out the present invention.

FIGS. 2-5 present schematics of the chamber of the preferred device.FIG. 2 provides a front view of the biobox chamber on the moveabletable. FIG. 3 presents a top view of the biobox chamber, while FIG. 4provides a side view. FIG. 5 provides front, top and side views of thebiobox off of the microscope.

FIG. 6 is a schematic representation of the pattern recognition softwareemployed by the invention. Panel A presents modeled data representing asingle cell one dividing cell, and two cells in contact, then twoseparated cells. Panel B presents the data derived from analysis of theobjects in panel A.

FIG. 7 is an overhead view of a representation of the movement of thetable to locate the sample well under the needle for fluid exchange withany point in the sample plate.

FIG. 8 is an overhead view of the movement of the table to locate theneedle in the wash and waste station in the chamber.

FIG. 9 is a schematic representation of a z-robot pipette and fluidicselements.

FIG. 10 is a schematic representation of the z-robot pipette andfluidics elements on the biobox.

FIG. 11 provides a schematic of an exemplary data analysis procedureemployed in the present invention.

FIG. 12 illustrates an evaluation of subpopulations of T lymphocytes.The left panel shows a single time image of the T lymphocytes. TheY-axis of the histogram in the right panel is the normalized populationfrequency, and the X-axis. is a fraction of the cells segregated bycurvelinear velocity.

FIG. 13 provides a schematic of the oncogenesis disease model.

FIG. 14 depicts an example of the primary immune response disease model.

FIG. 15 provides a schematic of the angiogenesis disease model.

FIG. 16 presents the results from one assay (PIR-1) from a primaryimmune response disease model. Fluorescent images were superimposed uponvisible light images (panels A and B) to align clusters of phagocytizedbeads with phagocytic dendritic cells (DCs). DCs were incubated for 24hours with 2-micron fluorescent polystyrene beads in the presence (panelA) or absence (panel B) of IL-1 beta (20 ng/ml) and tumor necrosisfactor (TNF). Cells containing fluorescent bead clusters of area greaterthan 60 square microns from duplicate wells are quantified in panel C.

FIG. 17 presents results from a second assay CPIR-2) from a primaryimmune response disease model. DCs were co-cultured with naive T cellsfor 24 hours and imaged every 3 minutes in the presence (panel B) orabsence (panel A) of 1 ng/ml superantigen Staphylococcal Enterotoxin B.The number of T cells (TC) within a single T cell diameter (see arrows,no outlines) of a dendritic cell (DC) were quantified for each image andplotted per DC in panel C. T cells are outlined that were not locatedproximal to a dendritic cell.

FIG. 18 provides results from a third assay (PIR-3) from a primaryimmune response disease model. DCs were co-cultured for 24 hours withnaive T cells (TC) in the presence (panel B) or absence (panel A) ofStaphylococcus Enterotoxin B superantigen (1 ng/ml) and then imagedevery 3 minutes. The ratio of cell length to breadth was calculated forevery cell in each image. Panel C plots the image averages.

FIG. 19 provides results from a fourth assay (PIR-4) from a primaryimmune response disease model. Primary lymphocytes were isolated fromperipheral blood and cultured in the presence (panel B) or absence(panel A) of IL-2, the protein of interest, at various concentrations(0.2, 1, 5, 25, and 100 ng/ml). Lymphocyte migration was quantified fromsingle cell tracking and plotted over time (panel C).

FIG. 20 provides another example of a primary immune response modelassay. DCs were cultured in the presence (panels B and D) or absence(panels A and C) of 50 ng/ml of TNF-alpha. The accumulated tracks formore than 300 images are shown by light lines. The average velocitiesfor the cells over the period are plotted (Panel E), with error barsrepresenting standard deviation.

FIG. 21 provides a 3-D graph showing that multiple assay determinationscan be obtained from a single sample plate.

FIG. 22 depicts, in schematic form the operations of an exemplarysoftware program useful for imaging cells in the present invention.

DETAILED DESCRIPTION

The present invention allows for the direct determination of thefunction of a protein. An automated system captures images of cells in awell within a biochamber of a closed environment. After a given cell isexposed to a protein of interest, the system measures the dynamic stateof cell, reflected in the responses of the cell over time to theprotein, by evaluating a variety of cellular parameters, at single-cellresolution. Analytical software within the system evaluates datagenerated by these measurements. By comparing the kinetic data from theexposed cells with various controls, the system elucidates the functionof a protein in one or more disease models.

The inventive method provides an efficient, cost-effective means fordetermining the function of a protein. In addition, protein function canbe determined without knowing the structure, gene sequence or chemistryof the protein. Furthermore, the invention streamlines the developmentprocess and reduces the cost of drug discovery by elucidating thefunction of a target protein during the earliest stage of development.The invention can be used for screening, discovering, analyzing andvalidating disease and health relevance of proteins.

In one of its aspects, the present invention provides methodology andcompositions for identifying lead developmental targets, in the form ofproteins that have functions of interest. To this end, a plurality ofproteins can be examined simultaneously by an automated system withinthe invention. Moreover, it is feasible to examine the effect of acombination of proteins on a particular cell type, as well as for avariety of disease models to be evaluated concomitantly.

In the present description, the terms “gene” and “structural gene” referto a DNA sequence that is transcribed into messenger RNA (mRNA), whichis then translated into a sequence of amino acids characteristic of aspecific polypeptide (protein).

The term “expression” denotes the process by which a polypeptide isproduced from a structural gene. The expression process involvestranscription of the gene into MRNA and the translation of such rnRNAinto polypeptide(s).

A “cloning vector” is a DNA molecule, such as a plasmid, cosmid,phagemid, or bacteriophage or other virally derived entity, that canreplicate in a host cell and that is used to transform cells for genemanipulation. Cloning vectors typically contain one or more restrictionendonuclease recognition sites at which foreign DNA sequences may beinserted in a determinable fashion without loss of an essential functionof the vector, as well as a marker gene that is suitable for use in theidentification and selection of cells transformed with the cloningvector. Appropriate marker genes typically include genes that providevarious antibiotic or herbicide resistances. A variety of markers areavailable to the skilled artisan.

The phrase “disease-model cell” refers to one or more cells from adisease state of interest. A disease-model cell can comprise more thanone type of cell. While they do not represent an exhaustive descriptionof a disease state, disease-model cells provide an overview of the keyevents associated with a particular disease, which can be monitored todetermine the function of a particular protein. Similarly, thedisease-model cells can provide an overview of the key states of ahealthy human without the particular disease of interest.

A “data set” is an assemblage of data generated for each cell regardingthe various parameters measured during the experiment.

A “modifying agent” affects at least one of the plurality of observablecharacteristics of a disease-model cell.

In a preferred embodiment, a disease model is selected first. Then, theassays used to quantify different parts of the disease model are chosen.The assays incorporate the various primary cells, cell lines andengineered cells utilized by the disease model. Next, the proteinlibrary is selected. The library can consist of, for example, peptides,secreted proteins or antibodies. The library can take the form ofisolated protein, such as those obtained using chromatography, 2D gelelectrophoreses and protein chips, or DNA, such as a cDNA library. Next,the proteins (or CDNA) are added to the disease-model cells. The methodof protein addition depends upon the specific form of the protein (orCDNA). If the protein is an antibody or protein supernate from a culturewell, it can be added into a specific well by pipetting. If the proteinneeds to be delivered into the interior of the disease-model cells, thenfusion protein methods, such as described in U.S. Pat. Nos. 5,804,604and 5,747,641, or viral methods, such as found in U.S. Pat. Nos.6,184,038 and 6,017,735, can be used. For cDNA, common transfectionmethods for incorporating cDNA sequences into cells can be used. Afterthe proteins are added to the disease model, the functional assays areperformed, and the quantitative data is collected using the imagingtechniques described herein.

In a preferred embodiment, the methodology of the present invention isaffected with the device described in U.S. Pat. No. 6,008,010, thecontents of which are hereby incorporated by reference. As shown inFIGS. 1-5, the device includes an incubating mechanism 200, whichpreferably includes a housing 204 having a Biochamber 10 in the housing204. The incubating mechanism 200 also preferably includes a first well206 and at least a second well 208 in which cells are grown. The firstand second wells are disposed in the Biochamber 10 of the housing 204.The incubating mechanism 200 preferably comprises a transparent plate207 in which the first and second wells are disposed.

The housing 204 preferably has a first port mechanism 210 through whichthe first and second wells in the Biochamber 10 can be viewed. The firstport mechanism 210 preferably includes a first window 209 disposed inthe top of the housing 204 and a second window 211 disposed in thebottom of the housing 204 and in optical alignment with the first window209 to form an optical path for light entering the first window 209 fromoutside the housing 204 and to exit the housing 204 through the secondwindow 211. The housing 204 preferably has a second port mechanism 214in fluid communication with the Biochamber 10.

Cells are maintained in individual wells of multi-well plates under asterile, controlled environment (i.e., physiological temperature, pH,pO₂ and humidity) inside an anodized aluminum Biochamber 10 with glasswindows on top and bottom to provide an optical path for imaging. Thereare two embodiments for the system 300: a Biochamber 10 (FIG. 1 andTable II) and a Biochamber 10 also with z-robot for medium exchange, asshown in FIGS. 7-10. The Biochamber 10 for the first embodiment(described in detail in FIGS. 2-5 and Table III) is approximately 6inches by 5 inches by 2 inches high. Temperature is regulated using andRTD 58, Temperature Controller 12, and Heating Cartridges 62. Media pHis maintained using standard bicarbonate-based buffers and a CO₂Controller 14, which sets atmospheric pCO₂ at 5% by regulating the flowof CO₂ from a CO. Supply Tank with Regulator 16 through a solenoidvalve, based on signals from a detachable CO₂ Sensor 66 mounted on theside of the Biochamber 10. Control of pO₂ in the Biochamber 10 can bemaintained similarly through a sensor and supply interfaced through twoadditional chamber front ports. The humidity is maintained by a heatedchamber 70 of sterile water to maintain close to 100% relative humidityinside the biobox and minimize evaporation.

In operation, before use the disassembled Biochamber 10 is sterilized byswabbing with a 70% aqueous solution of ethanol in the sterileenvironment of a laminar flow hood. The multi-well plate 207 ismaintained at 37° C. in a humidified atmosphere of 5% CO₂ while thecells are plated. The procedure for plating cells is describedsubsequently in this application. Spare wells in the plate in whichcells were not plated previously are filed with 100 μL of steriledistilled water to maintain 95-100% humidity inside the enclosedChamber. The CO₂ Sensor is mounted on the right face of the Chamber Body50 by tightening two 1½× 3/16-inch, hexnut-headed screws. The CO₂ lineis attached using a quick connect fitting 72 to the ⅛ diameter nylonsupply line. Next, the plate 207 is placed carefully into the inset onthe bottom of the Chamber Body 50 and secured with a spring clip. TheChamber is enclosed by placing the Chamber Cover Gasket 56 in a grooveon the top face of the Chamber Body and securing the Chamber Cover 52 inplace on top of the Chamber Body and Chamber Cover Gasket by tighteningsixteen 0.50×0.19-inch, hexnut-headed screws. Chamber assembly iscompleted by securing the two Heating Cartridges 62 into channels inside walls. of the Chamber Body from ports in the front face of theChamber Body using one Heating Cartridge Retaining Screw 64 each.

Environmental control within the Biochamber 10 is maintained byregulating temperature and the partial pressure of CO₂ with two controlsystems. The RTD 58 is connected by insulated electrical wire to theinput junction of the Temperature Controller 12. The two HeatingCartridges 62 are connected by insulated electrical wire to the outputjunctions of the Temperature Controller. The RTD 59 is connected byinsulated electrical wire to the input junction of the TemperatureController 17. The two Heating Cartridges 65 are connected by insulatedelectrical wire to the output junctions of the Temperature Controller,controlling the temperature of the table 18. The RTD 60 is connected byinsulated electrical wire to the input junction of the TemperatureController 15. The two Heating Cartridges 67 are connected by insulatedelectrical wire to the output junctions of the Temperature Controller,controlling the temperature of the humidity generating chamber 70. TheCO₂ Sensor 66 is connected electrically to the input junction of the CO₂Controller 14. The output gas stream from the CO₂ Sensor is connected tothe CO₂ Supply Fitting 68 on the front face of the Chamber and the CO₂Supply Tank with Regulator 16 connected to the input gas stream to theCO₂ Sensor. The assembled Biochamber 10 with environmental controls isallowed to thermally and atmospherically equilibrate for one to twohours before placement on the Motorized Stage 18. Temperature and pCO₂are controllable to 37±0.5° C. and 5±0.2%, respectively, over the courseof several days.

The Biochamber 10 with environmental controls next is secured on theMotorized Stage 18 with a spring mount. Cells for observation are chosenautomatically by the software based upon user inputted parameters. Foreach well, one or more fields are selected. After selection of fieldsfrom up to preferably 96, 384 or 1536 (or more) wells for observation,the user initiates the automated imaging and analysis by selecting theappropriate option. Each field selected then is scanned sequentially ata user-defined interval, preferably between one and 60 minutes. It alsois possible to scan at shorter or longer intervals depending on therequirements of a particular biological system. Each field is imagedunder phase-contrast optics with transmitted light illumination usingthe Video-Rate CCD Camera 32 and under fluorescence optics withepillumination, using the Cooled CCD Camera 34.

In a preferred embodiment, the dynamic state of each cell is evaluatedusing a robotic imaging system. Cells are observed using an InvertedMicroscope 20 with extra-long working distance (ELWD) condenser andphase-contrast objectives and epifluorescence attachments. Digitizedvisible and fluorescence images of cells are obtained using a Cooled CCDCamera 34 connected directly to an interface board in the a Pentium 1.8GHz PC. Imaging operations on the PC are performed using two softwareprograms: ImagePro Plus, Version 3 (Media Cybernetics, Silver Spring,Md.) and CellMonitor, which has the functionality described in FIG. 22.

ImagePro controls the filter wheels, shutters and stage position throughthe serial interfaces of each module. CellMonitor communicates withImagePro to run an experiment on the instrumentation. The programprovides a user interface for viewing various locations on a plate. Theoperator determines-the position and focus. After all the locations aredetermined, the program sends commands to Imagepro to define a location(X,Y coordinates) and a focus position. Commands are then sent to theLud1 controllers to locate a specific location and focus by sendingspecific instructions through the serial interface to the Lud1controller. For a specific location, the operator can specify a visibleimage at a specific exposure. CellMonitor sends an instruction set toImagePro to open the visible lamp shutter and to the camera to take animage. The image is displayed in both ImagePro as well as CellMonitor.The image is also saved to memory for later use. The location and nameof the image is defined by CellMonitor, which instructs ImagePro tostore the image. At each location, the operator may also require afluorescent image. In this case, instructions are again sent to ImageProto move the filter wheel to a specific location and close the visiblelight shutter and open the fluorescent shutter. The camera is instructedby CellMonitor through ImagePro to take an image and again display andstore the image. The communication to the Lud1 controller is a serialset of instructions sent from ImagePro as instructed by CellMonitor. Itis also possible to communicate directly to the Lud1 controller directlyor by a pass through of commands to the Lud1 controller. This method isused to send multiple signals to the controller and overlap the stagemovement with the filter wheel and shutter operations to speed up theoperation. CellMonitor provides the sequence of events necessary to moveto a location and take various images that are stored on the computerfor later analysis of the images. The events are timed based of arequired scan time or group of locations as well as by cellular events.

CellMonitor also provides image processing of an image if required bythe operator. In one application of the system, a cell in a specificwell can be tracked. Since cells move within the wells, it is possiblefor a cell of interest to move out of the view field of the image, if itis not tracked. The operator locates a cell of interest and the programtakes a digital image. This image is a series of pixels each with avalue from 0 (black) to 256 (white). This gray scale image representsthe cell and surrounding background. A typical image is 658 (xcoordinate) by 517 (y coordinate) pixels of information. Based on themagnification on the microscope, a pixel will represent a specific sizein the plate. For example, at 20× magnification on the microscope, apixel will represent 1 micrometer (micron) by 1 micrometer (micron).While cells vary in size depending on the type, a typical cell is about10 microns in diameter. Using lighting methods common in microscopy, theedge of the cell, as well as the cell itself, can be adjusted to bebrighter or darker than the background of the image. This is defined ascontrast in the image. CellMonitor loads the recorded image. andtranslates it into an array of pixel values for a given location. Byimplementing various image processing techniques, the edge of the cellcan be enhanced as the background is flattened or smoothed. The cell isthen identified by locating objects in the image of a specified size orcharacteristic and rejecting all others. For example, a cell (object)should be 5 to 20 microns in diameter and be should be close to round.All other objects, irregular or too large are rejected. A second blackand white image is then generated identifying likely objects in thatimage. Based on the location of the object (cell) in the previous image,the object in the current image is selected. The location is based on 2parameters, location and cell area.

If the cell is moving, it will not be in the middle of the image.Therefore, the coordinates of the cell in the current image are used asthe center location sent from CellMonitor to ImagePro for the nextlocation. If the cell does not move out of the image by the time thenext picture is taken, then the tracking/scan time is correct. Thisimage processing of the image also can be used to detect a change in thecell characteristics. The cell can change shape, for example, beforedividing. In that case the cell rounds up and then it divides into 2objects. At that point, CellMonitor declares division. The center ofthe, well location is sent to ImagePro, to center the needle over thewell where the cell has divided.

CellMonitor sends serial instructions to the needle drive to move to aspecified location in the well to stain the cell. Cell staining involvesremoving liquid from a well and replacing that liquid with a secondliquid containing an antibody. After incubation, the antibody is removedand a fluid used to dilute the stain. CellMonitor instructs the fluidicsvalves and syringe for these operations through serial instructions tothe various modules. At various points in the process, positions areverified by optical sensors sent to the DataForth modules, to verifypositions as well as to turn on and off pumps for cleaning and wasteremoval. These instructions are also serial instructions to the modules.After the staining/fluidics process, CellMonitor instructs ImagePro totake visible and fluorescent images of the cells, indicating thecondition of the cell/cells. Both phase-contrast/no phase visible andfluorescence images are captured and processed then stored on thecomputer's hard drives.

The robotic components of the imaging system (FIG. 10) are controlled bya Microscope Controller 28 which itself is controlled by commands fromthe PC, through an RS-232 interface. The Biochamber 10 is secured on aMotorized Stage 18 mounted on the Inverted Microscope 20. The MotorizedStage 18 has a resolution of 0.1 μm, an accuracy of ±6 μm, and arepeatability of 1 μm. Preferably, the Biochamber 10 itself withMotorized Stage 18 mounts directly on the Inverted Microscope 20. Focuscontrol is achieved for each well using a Motorized Focus Drive Assemblyand Controller 22 mounted on the focusing knob of the InvertedMicroscope 20. Illumination is switched between transmitted light forphase-contrast imaging and epillumination for fluorescence imaging usinga High-Speed Shutter for Transmitted Light 24 and a High-Speed DualFilter Wheel with Shutter for Fluorescence 26. The Motorized Focus DriveAssembly and Controller 22, the motorized stage 18, the High-SpeedShutter for Transmitted Light 24, and the High-Speed Dual Filter Wheelwith Shutter for Fluorescence 26 are connected electrically to theMicroscope Controller 28. Initial x-y positioning of the Motorized Stage18 stage and z-focal planes for each well are chosen by software anduser programming on computer 42 or can be chosen using a Joystick 30connected to the Microscope Controller 28.

The z-robot pipette dynamically controls the composition of medium whichbathes cells by automatically adding growth and/or quiescence factors toindividual wells based on cell behavior. Software driving the operationof this z-robot pipette is integrated with software for monitoring cellbehavior. (Refer to FIGS. 7-10) The system 300 also can add, remove orchange medium based on external criteria, such as at particular timeintervals chosen by the user. The z-robot pipette also transfers mediafrom individual wells to supplemental analysis systems. The z-robotpipette for media exchange itself consists of a modified micropipettetip, see FIG. 9, mounted on a support arm driven by a z-axis steppermotor to move up and down and raise and lower the pipette tip foraspirating and dispensing media in 0.2 to 95% increments. Although 100μL of medium typically is added to each 300 μL-volume well, aspiratingall of the medium from a well can result in large shears being appliedto the cells, which can detach or otherwise disturb them. Preferably, aminimum volume of 5 μL (corresponding to a depth of 125 m) of mediumremains in each well at all times.

The major component of the pipetting system consists of a syringe pump100 that can deliver growth factors, quiescence factors, or any type ofliquid from multiple fluid reservoirs 101 through tubing to a pipettetip 102. The syringe pump consists preferably of a 250 microlitersyringe 103 (although other syringe sizes can be used) that is driven bya stepper motor 104, which is in turn controlled via a multi-portstepper motor driver card 105 and a computer 42. The stepper motor 104drives the plunger 107 of the syringe 103 up and down which results in adispensing action (if the plunger is being driven into the syringe) oran aspiration action (if the plunger is being driven out of thesyringe). The syringe is connected to one port of a distribution valve108. The distribution valve can be from 3ports to 8 or more ports. Oneport is connected to the syringe 103, one port is connected to thepipette probe 102, one port to an optional wash pump 111, and theremaining ports to various fluid reservoirs 101. The distribution valve108 is also stepper-motor driven through stepper motor 109 which can bedriven also from stepper motor drive board 106. The syringe, steppermotor, stepper motor driver, and distribution valve can be obtained fromAdvanced Liquid Handling model MBP 2000 (Williams Bay, Wis.). A seconddistribution valve also can be mounted in the system in parallel withvalve 108 to tie into more fluid reservoirs. The reservoirs 101 arethermostat to 4±2 C. by a thermostatting means 112, to allow goodpreservation of the growth and quiescence media and tied to thedistribution valve 108 through 1/16 inch Teflon tubing.

The distribution valve (and thus the syringe pump) is plumbed via 1/16inch-Teflon or stainless steel tubing to the pipette probe 102. Thepipette consists of a stainless steel probe with an ID of 1/32 inch(0.031 inch) that narrows down to a tip D of 0.013 inch This pipette tipis used for both dispensing growth and quiescence factors into the 96well plate as well as aspirating media out of the plate. The pipetteprobe has conductive coating on the outside of the probe that provides asignal that can be read by the computer 106. This electrical signalprovides feedback on how much fluid there is in a well, consequently,when aspiration should stop. The pipette probe is driven in the “Z”direction by a stepper motor 110 that is tied into the stepper motordrive 105. This stepper motor drives the pipette probe up and down todispense into or aspirate out of a selected well. The probe withconductive sensing can be obtained from Diba Industries, Inc., (Danbury,Conn.). The pipette stepper motor can be obtained from Advanced LiquidHandling model MBD Crawler (Williams Bay, Wis.). The pipette probemounts into the biocontainment box by piercing through a Teflonbulkhead. The Teflon bulkhead has a hole in it that is sized tointerference fit the OD of the pipette probe. Thus a seal is madebetween the OD of the pipette and the E) of the hole in the Teflon. Thisfit allows the pipette to move up and down freely and yet provides aseal to keep the environment within the Biochamber stable. The pipettemoves down into the well to a depth of 3±1 mm from the top of the wellfor dispensing; the pipette moves down to the liquid surface in the wellfor aspiration (as measured by the conductive sensing mechanism on theprobe tip); and the probe moves up out of the well with a clearance of10 to 13 mm to clear the well as the well plate moves around on the x-ystage.

In an alternative embodiment, multiple dispensing/aspiration tips areutilized in parallel to dispense or aspirate a 96, 384 or 1536 wellplate, thereby achieving higher throughput. A wash is performed toremove growth factors, quiescence factors or used media from theplumbing lines. The preferred wash fluid is Phosphate Buffer Saline(PBS). One approach is to use one of the reservoirs 101 for wash fluidto clean the system. Another approach is to use a separate wash pump 111with the system. The wash pump 111 is a peristaltic pump with highervolumuetric flow capabilities that can be turned on by the computer 42and pump through higher flows of wash fluid. The wash fluid is dispensedfrom the pipette tip 102 to a flush station within the Biochamber 10, asshown by item 330 in FIG. 9.

Fluid transfer in the Biochamber 10, involves location of the needleover a specified well in the plate. See FIG. 7. The needle is loweredinto the well, and fluid is added or removed from the well. The needlethen retracts and the table moves to another location under the needleor the needle is sent to the waste/cleaning station 330. See FIG. 8. Thesterile fluid dispensed from the needle along with any waste fluids aresent to the waste vial 113, with a waste removal pump 112. Refer to FIG.10 for a view of the fluidics components on the Biochamber.

The occurrence of cell division and differentiation is detected bypattern recognition software. The software detects multiple otherparameters including, but not limited to, 1) path of locomotion of acell; 2) spread of cell movement 3) cell contact interactions in realtime with other cells or objects; 4) and indirect cell responses (i.e.,protein production). The number and two-dimensional shape (e.g., areaand perimeter) of “objects” in each selected field are identified fromphase-contrast images after application of an optical gradienttransformation, thresholding, and dilation to detect each cell (see FIG.6). Threshold values for shape parameters which indicate whether eachobject comprises one or more cells have been defined. The number ofcells then is determined in each well at that particular time point bycomparing the current values of the shape parameters with values forprevious time points. Cell division is detected automatically as anincrease in cell number between two time points. Image analysis alsoprovides information on (x-y) positions which can be used to measureindividual cell speed and directional persistence time by application ofa persistent random walk model for migration, to determine the fractionof a population which is motile, and to adjust the position of the fieldto allow for cell movement while centering cells in the field.

The parameters of cell speed and directional persistence time for eachcell, as well as the %-motile for a population of cells, are determinedby fitting a mathematical model for a persistent random walk in anisotropic environment to observe data for the mean-squared displacementof each individual cell based on a time sequence of (xy1 position at thecontrol of the cell). For example, see DiMilla et al. AIChE J.38(7):1092-1104 (1992); DiMilla et al., Mater. Res. Soc. Proc.,252:205-212(1992); DiMilla et al., J. Cell Biol., 122(3):729-737.(1993); DiMilla, Receptor-Mediated Adhesive Interactions at theCytoskeleton/Substratum Interface During Cell Migration, in CELLMECHANICS AND CELLULAR ENGINEERING (Hochmuth et al. eds., 1994); Thomaset al., Effects of Substratum Compliance on the Motility, Morphology,and Proliferation of Adherent Human Gliblastoma Cells, in 29 PROCEEDINGSOF THE 1995 BIOENGINEERING CONFERENCE, at 153 (R. M. Hochlmuth et al.eds., 1995), all of which are hereby incorporated by reference.

In determining the state of each cell over time, the imaging system canevaluate a variety of cell parameters concomitantly. In a preferredembodiment, measurement is made of over 65 parameters for each cell ineach view field. Illustrative of such parameters are those detailed inTable I. TABLE I Parameters Measured Suggested Type of Measurement NameParameter Description Reference 1. Colony count Object CountProliferation, The number of objects in an image, where (1-2) apoptosiseach object is a separated region within the image outlined on the basisof cell-like characteristics. 2. Object count Cell count 1Proliferation, The number of individual cells in an image, apoptosisdetermined by dividing each object area (parameter 1) by a user definedaverage area for a cell. 3. Proliferation Cell count 2 Proliferation,The number of cells in a view field, count apoptosis determined by firstdetermining the average of all objects within 3 times the presetpreferred cell size. Then dividing each colony object by that averagearea to get a total cell count. 4. Vinst(abs) Instantaneous Motility Theaverage of the Vinst values for all tracked (1-2) Speed cells in animage (see Vinst, below). 5. Vinst(angle) Instantaneous Motility Theangle of the vector sum of the (1-2) Direction displacement of the cellposition between the first and second points and between the second andthird points. 6. Vinst Instantaneous Motility The vector sum of thedisplacement of the cell (1-2) velocity position between the first andsecond points and between the second and third points divided by theelapsed time between the first and third points. 7. Vavg_inst(abs)Instantaneous Motility The instantaneous speed of the average (1-2)Smoothed smoothed track through a specified number of Speed imagesbefore and after the specific image. 8. Vavg_inst(angle) InstantaneousMotility The angle of the instantaneous speed, #7. (1-2) Smoothed angle9. Vavg_inst Average Motility The average of a specified number ofimages (1-2) Instantaneous of the smoothed track at a specific time/Velocity image. 10. Vsl(abs) Straight Line Motility The straight-linevelocity of the average (1-2) Speed smoothed track. 11. Vsl(angle)Straight Line Motility The angle of the straight-line velocity, #10.(1-2) Angle 12. Vsl Straight Line Motility The straight-line velocity ofthe instantaneous (1-2) Velocity speeds of the track. 13. VclCurvilinear Motility The change in the average velocity over the (1-2)Speed full track up to a specific field. 14. Vavg Average Motility Thechange in the average velocity of the (1-2) Velocity smoothed track to aspecific field. 15. Linearity Linearity Motility The straightness of acells motion, Vsl/Vcl. (1-2) 16. Straightness Smoothed Motility The sameas linearity, using the smoothed (1-2) Linearity track, Vsl/Vavg. 17.ALHmean Amplitude Motility The measure of the oscillating amplitude ofan (1-2) objects motion. The average amplitude of the track oscillationsaround the smoothed track. 18. ALHmax Maximum Motility The maximumamplitude of the oscillating (1-2) Amplitude component of the cellsmotion around a smoothed track. 19. BCF Beat Cross Motility The averagenumber of oscillations about the (1-2) Frequency average track. 20.Circular Morphology A measure of the circular component of the (1-2)radius objects motion. 21. Filtered Proliferation, The number of objectsthat are filtered from objects apoptosis the analysis, based on theirindividual speed. 22. % motile Percent Motility The percentage ofobjects that is more motile [1-2] Motile than a given area per image.23. Elongation Elongation Morphology The ratio of the length to thewidth of an (3) (avg) Rectangle, object based upon the ratio of theperimeter to Elongation the area in a rectangular model (ElongationEllipse, Rectangle) or an elliptical model (Elongation ElongationEllipse) or upon actual cell widths determined Feret throughout a set ofangles (Elongation Feret) 24. Start image Track Experimental The firstimage for which a cell position is Segment Start included in a specifictracked. 25. End image Track Experimental The final image from which acell position Segment End was included in a specific track. 26. CyteCyte Morphology An imaging position and an associated computer foldername used for acquiring and storing images. 27. Avg Area Average AreaMorphology The average area of all the objects determined (3) Pixels orfrom an image. Average Area Microns 28. Min Area Minimum Morphology Theminimum area (in pixels or microns) of (3) Area Pixels an object in atrack or time series. or Minimum Area Microns 29. Max Area MorphologyThe maximum number of pixels or microns of (3) an object in a track ortime series. 30. Mean Morphology The average gray scale intensity of thepixels (3) intensity within an object. 31. Intensity Sum Morphology Thesum of all the pixel intensities within an (3) object 32. Object PixelMorphology The standard deviation of the intensity of all (3) SD thepixels within an object. 33. Area Area Pixels Morphology The number ofpixels in an object or the area (3) or Area in square microns of anobject. Square Microns 34. X coord Motility The x coordinate of thecenter of an object in (3) an image. 35. Y coord Motility The ycoordinate of the center of an object in (3) an image. 36. PerimeterPerimeter Morphology The sum of the pixels around the perimeter of (3)Pixels an object. 37. Fmax Morphology The maximum width of an objectafter the (3) Diameter angle is swept by a specified preset angle. 38.Fmin Morphology The minimum width of an object after the (3) Diameterangle is swept by a specified preset angle. 39. Length Length MorphologyThe maximum width of an object based upon (3) Rectangle fitting theperimeter and area to a rectangular model. 40. Breath Breadth MorphologyThe minimum width of an object based upon (3) Rectangle fitting theperimeter and area to a rectangular model. 41. Elongation(L/ ElongationMorphology The length/breath based upon fitting the (3) B) Rectangleperimeter and area to a rectangular model. 42. Convex Morphology Theapproximation of a convex hull of an (3) Perimeter object based on aswept angle. 43. Compactness Morphology The roundness of an object,perimeter squared/ (3) (4 pi Area). 44. Roughness Morphology Measure ofthe irregularity of the perimeter. (3) Perimeter/convex perimeter. 45.FElongation Elongation Morphology The Fmax/Fmin. (3) Feret 46. EnergyMorphology A measure of the variation of the intensity of (3) an object.47. Mean Energy Morphology The average variation in intensity of anobject. (3) 48. Density Morphology The accumulation of the number ofvariations (3) divided by the area. 49. Density Sum Morphology The sumof all the variations within an object. (3) 50. Unique TrackCell-specific A unique number for each track generated Index Delimiterfrom cell-like objects in a series of images. 51. Track SizeCell-specific The length of a track in terms of the number Delimiter- ofcell positions included. Motility 52. Track Cell-specific The larger ofthe x or y displacements, in Boundary Delimiter- pixel widths, of cellpositions along a track. (pixels) Motility 53. Fluorescent Selected Theintensity sum of an object, based on a (4) marker 1 protein fluorescentmarker, TRITC. expression Note: Filter sets for detecting various markerfor fluorophore can be purchased from: Chroma phenotype Technical Corp.72 Cotton Mill Hill, Unit A9 Brattleboro VT 05301, USA 54. FluorescentSelected The intensity sum of an object, based on a (4) marker 2 proteinfluorescent marker FITC. expression marker for phenotype 55. FluorescentSelected The intensity sum of an object, based on a (4) marker 3 proteinfluorescent marker DAPI. expression marker for phenotype. 56.Fluorescent Selected The intensity sum of an object, based on a (4)marker 4 protein fluorescent marker CY5. expression marker for phenotype57. Proximity Cell-cell The number of cells of Type A that interact or(Cell to cell interactions touch a second cell of Type B, based on acontact) (e.g., antigen distance from the perimeter parameter of cellpresentation) Type B. 58. Frequency of Cell-cell The rate of cells oftype A coming into Proximity interactions proximity with a cell of typeB. (e.g., antigen presentation) 59. Duration of Cell-cell How long thecells of type A stay in contact Proximity interactions with a cell oftype B. (e.g., antigen presentation) 60. Cell-Specific Cell-cell Thenumber of cells interacting with a second Proximity interactions cell ofa specified morphology. (e.g., antigen presentation) 61. PhagocytosisCell-cell The number of fluorescent beads (antigens) (5) Attachmentinteractions that are attached to a cell. (e.g., antigen presentation)62. Phagocytosis Cell-cell The number of fluorescent beads (antigens)(5) Engulfed interactions inside a cell. (e.g., antigen presentation)63. Phagocytosis Cell-cell The area of fluorescent beads (antigens) that(5) Attachment Area interactions are attached to a cell. (e.g., antigenpresentation) 64. Phagocytosis Cell-cell The area of fluorescent beads(antigens) inside (5) Engulfed interactions a cell. Area (e.g., antigenpresentation) 65. Persistence Motility Based on the random walk model,the time a cell proceeds in a given direction at a consistent speed.

Note that for parameters 53 through 56—Fluorescent markers—explanationsare given in Table 1 of prominent fluorescent markers. This inventioncan use any type of fluorescent markers that can be added based upon theavailability or design of the specific markers and the availability ordesign of specific filters that allow that fluorescent output to bedetected. Although four florescent outputs are shown in Table 1, filterset combinations can be purchased or designed that allow eight or eventwelve simultaneous florescent markers to be used and detected in thisinvention.

Data acquired from thousands of recorded images provide quantitativeinformation regarding the kinetics of cell movement, cell division,apoptosis, morphology, adherence, and physiologic function. The kineticsof each assay can be measured typically to a resolution of “minutes” and“per unit cell.” Population studies yield information on cell-cellsynergistic effects, the fraction of cells responsive and groupthresholds. Motility assays provide cell movement over time, direction,and cell phenotype.

According to one embodiment of the invention, apoptotic and mitoticevents are detected with visible light images. Apoptotic cells arerefractile for a much longer period of time than mitotic cells. Bydetecting the “bright refractile” objects in the image and examining thetrack lengths, i.e., the amount of time the “bright refractile” objectpersisted from image to image, produced by these objects, the frequencyof apoptotic events can be determined. Data analysis software producestrack length data for every track (cell) and exports the information toa database. As cells undergoing apoptosis consistently possess longertrack lengths than normal cells, the software can readily detectapoptotic events by identifying “bright refractile” objects with longtrack lengths.

The same technique can be used to automatically count mitosis. Celldivision produces short track lengths. Since there is a certain amountof back ground noise generated when cells move, the track lengths usedfor mitotic events must be longer than the tracks of the background andshorter than the tracks of apoptotic events. This technique provides amore accurate account of cell proliferation than counting total cells ina view field, which often yields inaccurate estimates when large numbersof cells are migrating in or out of the view field.

A data set, representing the various parameter values recorded duringthe experiment, is generated for each cell. The data can be presentedfor evaluation in a variety of formats. Combinatorial andmulti-parametric assays yield highly informative results.Two-dimensional plots reveal cell sub-population responses and offeruseful perspectives, often revealing subtle or unexpected responses,which can be referred to as “unexpected biology.” A database of results,comprising the various data sets, is automatically constructed to allowfurther data mining as additional mathematical analyses are devised.

The combination of data sets from various disease-model cells isanalyzed by bioinformatics software, which automatically compiles aknowledge base of protein, cellular and molecular relationships. Theknowledge base enables scientists to ascertain protein function and toconduct in silica testing, using computer modeling. Upon completion ofthe data analysis, the system can generate a report summarizing thefindings.

An exemplary data analysis scheme is depicted in FIG. 11. After the dataacquisition, a Quality Control Step I (QC I) is performed. This stepstatistically evaluates the viability and density of the cells. Testsalso verify that the sampling rate/resolution is sufficient for suitablemotility measurement. The cells within a specific assay must be viable,i.e., growing and functioning normally, and must have a density (howclose or far away cells are from one another) such that the imageacquisition can provide appropriate information. If these criteria arenot met, the assay is adjusted, for example, by increasing the samplingrate or by repeating the test with suitable cells before, image analysisand processing.

The data analysis system processes the sequential images, both visibleand fluorescent, to identify the cells within the image and to quantifythe multiple parameters for each cell within each image. The imageprocessing software quantifies the parameters of Table I for each cellwithin the specific viewfield of the imaging system. Each cell istracked from one frame to the next image and related to one anotherthrough its track. This tracking is accomplished by the softwareselecting a “given” cell in the first image and quantifying all theparameters of Table I for that cell. Then the software selects a set ofcells in the second image that are in proximity to the given cell of thefirst image in terms of x-y positioning. The software determines all theparameters of Table I for the selected set of cells in the second imageand compares those parameters of the given cell of the first image. Thesoftware then selects one cell from the second image as statisticallythe same as the given cell of the first image. This threshold ofstatistically similarity can be set at different levels of statisticalconfidence, such as 95, 99, or 99+ percent. If the software does notdetect the chosen level of similarity, then that track is stopped at thefist image. The level of statistical similarity can be increased byacquiring images at more closely spaced times. All of the parameters ofTable I are calculated for each cell within the image viewfield. In thedatabase-import step, the processed data are exported from theprocessing software and imported into a database. The database storesall of the separate mathematical parameters from each cell, in each wellor in each view frame.

Next, in the Quality Control Step II (QC II); the system identifies andremoves “nonsense” outliers from the data sets. A number of factors mayproduce nonsense outliers, such as mechanical irregularities of thevisible or fluorescent lighting, mechanical stage noise from the XYstage, sample well edge distortion, and power outages. After thesoftware identifies the outliers, a technician reviews the excisedoutliers and removes the data from the database. Alternatively, the datacan be re-processed and then re-imported into the database.

In the Quality Control Step m (QC III) statistical outliers are removedfrom the data. Statistical outliers represent real data but, forstatistical precision, are removed from the data analysis. Statisticaloutliers are identified using established methods such as Z-scores orMAD scores. See e.g. Robert R. Sokal & F. James Rohlf, BIOMETRY, THEPRINCIPALS AND PRACTICE OF STATSTICS IN BIOLOGICAL RESEARCH, 3^(rd)Edition, W.H. Freeman and Company, New York.

Next, the system conducts a statistical analysis of the data. Protein-,chemical- or biological-mediated wells are compared to control wells,and significant parameter changes are identified and analyzed. As thesystem identifies significant changes in variety of parameters in TableI, it provides a wealth of information regarding the physiologicaleffects and, hence, the function of proteins of interest. Thus, bycomparing the kinetic data from the exposed cells with various controls,the system elucidates the function of a protein in one or more diseasemodels. This information is then used to prioritize the proteins in alibrary. The proteins are prioritized by ranking the statisticaldifference in parameters between the protein. mediated well and thecontrol biology well. The parameters used to prioritize the proteinsdepend upon the disease model and the parameters that are mostindicative of the disease state. Alternatively, methods such as clusteranalysis can be used to stack rack a number of the protein parametersconcurrently. The data produced also validates the function of thespecific protein of interest in terms of the disease model and theprotein's relationship to a disease or healthy state in humans.

The inventive methodology also can provide useful information regardingthe disease model itself. In this regard, identification of thesignificance of a previously overlooked or unappreciated parameter, socalled “unexpected biology,” can greatly enhance the understanding of adisease model and provide a foundation for additional research.

In addition, the system enables the identification and characterizationof subpopulations. For example, FIG. 12 illustrates an evaluation ofsubpopulations of T lymphocytes. In this figure, an assay was conductedusing T lymphocytes as a disease-model cell. All of the parameters inTable I were measured for up to 72 hours. The left panel of FIG. 12shows a single time image of the T lymphocytes. The histogram picturedin the right panel shows two distinct subpopulations of the Tlymphocytes. The Y-axis is the population frequency and the X-axis is afraction of the cells segregated by curvelinear velocity. Thus, thecells are segmented into slow movers (to the left of the histogram) andthe fast movers (to the right of the histogram). Any of the parametersof Table I can be screened for subpopulations. Thus, multiparametricanalysis extends the breadth of information obtainable and increasesassay sensitivity.

A variety of disease-model cells can be used in the assays of thepresent invention to elucidate protein function. For example, theoncogenesis disease model can be used to elucidate a protein's functionwith respect to specific components of oncogenesis. FIG. 13 providesschematic of the disease model. Besides a cancer cell-line of interest,the model encompasses T-lymphocytes, B-lymphocytes, natural killercells, dendritic cells, monocytes and macrophages. Assayed componentsinclude antigen-specific tumor cell killing, tumor cell apoptosis, andvarious components of anti-tumor immunity, such as antigen presentationby dendritic cells and T lymphocyte recruitment. Quantitative endpointsinclude the stimulation or suppression of cell migration (chemotaxis),cell proliferation, and cell-cell interaction and the stimulation orinhibition of cell death. The discovery of a statistically significanteffect establishes a functional role in oncogenesis for the protein ofinterest. All of the parameters in Table I are measured for each celland type of Gell in the disease model.

In another example, the primary immune response disease model detectsthe function of a protein with respect to specific components of immunedisease. See FIG. 14. Exemplary immune diseases include, but are notlimited to, inflammatory diseases, such as rheumatoid arthritis andinflammatory bowel diseases, and autoimmune diseases, such as Type Idiabetes, multiple sclerosis and lupus. Relevant cell lines includeT-lymphocytes, B-lymphocytes, natural killer cells, dendritic cells,monocytes, macrophages, and cell-lines that are relevant to the immunedisease under consideration. The relevant quantitative endpoints can besimilar to those identified for oncogenesis, relating to cellularchemotaxis, cell proliferation, etc. The role of candidate proteins inthe effector phase of immune cell function, e.g., tumor cell killing,also is assayed. The functional maturation and differentiation ofvarious immune cells also can be assayed for hundreds of cells at asingle-cell resolution level. The discovery of a statisticallysignificant effect establishes a functional role for the protein ofinterest in the immune disease.

In addition, so-called “secondary immune response” models can becreated. Examples include, but are not limited to, 1) comparing T celland B cell responses after antigen challenge, 2) comparing thefunctionality of a patient's cells with a control population of similarcells (e.g., dendritic cells; T or B lymphocytes, etc.), 3) observingresponses to “blocking factors” or drugs, and 4) evaluating the effectof stimulating or suppressing the patterns of immune response (i.e., thephenotypical outputs as measured by the invention) by the presence ofknown or unknown proteins or drag candidates.

Such assays serve to categorize responses in patterns that definecertain disease states. The responses can be compared to pretreatmentdata to determine the success of a therapy or to pools of data from cellsamples from a variety of individuals to define disease subtypes andresponse patterns. Such data is useful not only to researchers but alsoto clinical practitioners for patient diagnosis, treatment and followup.

In yet another embodiment of the invention, the angiogenesis diseasemodel elucidates protein function with respect to specific components ofangiogenesis, which is the process of developing new blood vessels (seeFIG. 15). Angiogenesis may be a desirable objective, as is the case withneovasculature of a transplanted organ, or it may be undesirable, aswith the neovasculature of a tumor. Accordingly, the discovery ofproteins that stimulate or repress angiogenesis can be instrumental tohandling a variety of potential pathologies associated, for instance,with organ transplantation, atherosclerosis and oncogenesis,respectively.

Angiogenesis involves a series of steps undertaken by endothelial cells.In order to form a new blood vessel, endothelial cells of existingvessels must proliferate, sprout, invade the immediate vesselenvironment by protease-mediated migration, invade the new site and formthe novel blood vessel. Each of these steps can be measuredquantitatively using in vitro assays and combined into multiparametricassays. Quantitative endpoints include endothelial cell migration,proliferation and morphological changes, such as sprouting. In addition,bioassays such as the formation of fluid-filled tubes, protease-mediatedextracellular matrix digestion and target organ invasion also can beperformed. The discovery of a statistically significant effectestablishes a functional role for the protein of interest in theangiogenesis-related disease.

Alternative embodiments utilize expanded disease models that can includeadditional assays conducted for an existing disease model. Diseasestates can be categorized and staged by similarity of response patterns.For example, patients can be defined as having a certain disease,disease in remission, or recurrent disease based on response patterns.Disease models can be combined to study, for example, common aspects ofmultiple disease states, such as inflammation. Moreover, the models cancontinue to be developed by tying genotype to phenotype to diseaseoutcome and by including more disease areas. Also, traditionalprotein-protein interaction assays, such as phage display and two hybridscreening, can be employed generally in the claimed invention.

In a preferred embodiment, more than one disease-model cell is evaluatedin a single experiment. The disease-model cells can be from the samedisease model, or separate ones. Alternatively, a single run of theinstant method can comprise multiple disease-model cells from multipledisease models. For example, a first disease-model cell of the inventivemethod may be from a primary immune response disease model and evaluatea protein's function in a co-culture environment. A second disease-modelcell also may be from a primary immune response disease model, but mayevaluate a protein's role in maturation. Alternatively, the seconddisease-model cell may be from a different disease model, such as theangiogenesis model. In yet another embodiment, the instantprotein-analysis method can comprise, in a single run, multiple assaysfrom multiple disease models or multiple assays that cover various partsof one disease model.

The instant invention is suited ideally for combinatorial experiments. Acombinatorial experimental approach is where a large number of differentexperiments, each with different parameters, are performed in oneexperiment producing many results in parallel. Such a design canencompass a variety of disease models, assays, specific cell lines,protein targets, media and experimental parameters. Thus, a single runemploying a plate with 96, 384, 1536 or more sample wells can evaluate aprotein by means of a variety of disease models, wherein a multitude ofassays are performed for each disease model and a myriad of parametersare measured for each assay.

In another embodiment, the present invention provides methods andcompositions for identifying lead targets for development, i.e.,proteins that have functions of interest. In this regard, a plurality ofproteins can be examined simultaneously by the disclosed automatedsystem. Ideally, the plurality is evaluated using a combinatorialdesign, such that each protein is evaluated using a variety of diseasemodels, wherein a multitude of assays are performed for each diseasemodel and a myriad of parameters are measured for each assay. In thisfashion, the automated system identifies particular proteins within theplurality that have functional traits of interest. Alternatively, theplurality of proteins can be added to one sample well, such that theplurality of proteins is studied in a disease model and the effects. ofthe plurality of the proteins are identified. If a desired effect isidentified, the plurality (or pool) of proteins can be deconvoluted bysplitting the plurality into in a smaller number of pluralities andre-running those pluralities through the disease model, or by splittingthe plurality into singular proteins and re-running those proteinsthrough the disease model. In either case, the pool is deconvoluted tothe point where the proteins of interest are identified.

Protein libraries can be created from a variety of sources, includingcDNA, protein chips, culture supernatants, transgenes, novel peptides,disease-specific sera and cell lines, and antibody libraries. Purifiedproteins and antibodies can be added directly to the culture medium.Alternatively, a given protein can be studied in its relevant cellularcontext by introducing the encoding polynucleotide into the cell type inquestion or by introducing the protein directly into the cells, asdescribed below.

In one embodiment, a protein of interest is brought into contact with adisease-model cell by inserting the protein's gene into the cell, forexample, by retroviral transduction or lipid-mediated transfection.Depending on the assay, cDNAs and other constructs are introduced eitherstably or transiently. Typically, clones of novel or potential candidatetarget molecules are prepared in single plasmid arrays and introducedinto cells. cDNA sequences encoding potential target proteins areidentified by sequencing and inserted into retroviral transfectionsystems for development. of permanent cell lines that produce the targetprotein. Any technique that transduces or transfects genes or proteinsinto cells may be used in this context. See Sambrook et. al., 1989,MOLECULAR CLONING, A LABORATORY MANUAL, Cold Spring Harbor Press, NY;and Ausubel et al., 1998, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, GreenPublishing Associates and Wiley Interscience, NY.

Other transfection methods can be employed as needed. For example, thelenteviral system can be used for nuclear delivery of a cDNA in restingcells or cells that have stopped dividing due to differentiation.Adenoviral vectors can be used when nuclear delivery is not crucial andcells are resting. In first-pass screening, or when assay endpoints arebrief (less than 3 days), lipid-mediated transfection is sufficient. Inother embodiments, genes can be “knocked out” by means such as antisenseor inhibitory RNAs or dominant negative. mutation, or by the use ofheterologous, inducible promoters.

The present invention is described further by reference to the followingexample, which is illustrative only.

EXAMPLE Use of a Primary Immune-Response Disease Model

A. Primary Immune Response Assay 1

This assay determines whether a protein is involved with dendritic cellmaturation and the interleukin-1 (IL-1) pathway of the primary immuneresponse. Under natural conditions, dendritic cells mature and loseviability. Remaining cells consume the expired cells throughphagocytosis. Thus, phagocytosis is indicative of dendritic cellmaturation and differentiation.

Interleukin-1 beta was evaluated via this disease-cell model. Dendriticcells (DCs) were generated from peripheral blood monocytes by culture inIL-4 and GM-CSF. For 24 hours the DCs then were incubated with 2-micronfluorescent polystyrene beads in the presence (panel A) or absence(panel B) of IL-1 beta (20 ng/rnl) and tumor necrosis factor (TNF).Fluorescent images were superimposed upon visible light images to alignclusters of phagocytized beads with phagocytic DCs.

Panels A and B of FIG. 16 depict all fluorescent beads with largerclusters arising from the phagocytosis of beads by DC. Cells containingfluorescent bead clusters of area greater than 60 square microns fromduplicate wells are quantified in panel C.

As shown in FIG. 16, IL-1 beta decreased the amount of phagocytosis inDCs. The effect of IL-1 can be separated from the effect of TNF by theuse of the appropriate positive and negative controls, such asincubation with either IL1 or TNF alone. Thus, the PIR-1 assay is aneffective tool for determining whether a protein is involved in the IL-1pathway and dendritic cell maturation and differentiation.

B. Primary Immune Response Assay 2.

This assay, a second example of a primary immune-response disease model,evaluates a protein's function in a co-culture environment. Inparticular, the assay evaluates a protein's capacity to influencedendritic cell-T cell interactions. The interaction of T lymphocyteswith antigen presenting cells, especially dendritic cells, is animportant step in antigen presentation.

DCs were cultured with T lymphocytes and exposed to the protein ofinterest, Staphylococcal Enterotoxin B. DCs were generated fromperipheral blood monocytes by culture in IL-4 and GM-CSF. The cells wereco-cultured with naive T cells for 24 hours and imaged every 3 minutesin the presence (FIG. 17, panel B) or absence (panel A) of 1 ng/mlsuperantigen Staphylococcal Enterotoxin B. Lymphocytes weredistinguished from dendritic cells using CytoWare® image analysissoftware. In FIG. 17, the number of T cells (TC) within a single T celldiameter (see arrows, no outlines) of a dendritic cell (DC) werequantified for each image and plotted per DC in panel C. T cells thatwere not located proximal to a dendritic cell are outlined.

FIG. 17 demonstrates that Staphylococcal Enterotoxin B influencesdendritic cell-T cell interactions. These results confirm the assay'sutility in identifying autoimmunogenic proteins, inflammatory agents andvaccine candidates.

C. Primary Immune Response Assay 3

This assay, a third example of a primary immune-response disease model,evaluates a protein's role in DC maturation. Changes in DC morphology,such as the ratio of cell length to breadth and spreading are indicativeof DC maturation. Such changes are believed to arise from the secretionof cytokines, e.g., interferons, TNF, etc., resulting fromantigen-specific TC-DC interactions.

In this assay, DCs were cultured with T lymphocytes in the presence FIG.18, panel B) or absence (panel A) of Staphylococcal Endotoxin B, theprotein of interest. DCs were generated from peripheral blood monocytesby culture in IL-4 and GM-CSF. The cells were co-cultured for 24 hourswith naive T cells (TC) and then were imaged every 3 minutes, with orwithout Staphylococcus Enterotoxin B superantigen (1 ng/ml). Lymphocyteswere distinguished from dendritic cells using CytoWare® image analysissoftware.

The ratio of cell length to breadth was calculated for every cell ineach image. The image averages, presented in panel C of FIG. 18, showthat the superantigen induced dendritic cell elongation. Accordingly,the assay provides an effective and sensitive means for evaluating thefunction of a protein with respect to antigen presentation, lymphocyteactivation, dendritic cell maturation, and involvement with signalingpathways.

D. Primary Immune Response Assay 4

This assay, a fourth example of a primary immune-response disease model,evaluates a protein's effect on T cell activation by analyzingparameters such as cell migration. As dose-dependent increases inlymphocyte migration are indicative of lymphocyte activation, the assayelucidates protein function in pathways connected with lymphocyteactivation, such as the interleukin 2 (IL-2) pathway. Such pathways playimportant roles in inflammation and autoimmune diseases, such asrheumatoid arthritis and multiple sclerosis. The motility assay also isuseful for establishing protein function in metastasis, angiogenesis,wound healing and tissue remodeling.

Primary lymphocytes were isolated from peripheral blood and cultured inthe presence (FIG. 19, panel B) or absence (panel A) of IL-2, theprotein of interest, at various concentrations (0.2, 1, 5, 25, and 100ng/ml) for the indicated time periods. Lymphocyte migration wasquantified from single cell tracking using CytoWare® image analysissoftware. These data confirm the role of IL-2 in lymphocyte activation.As shown in panel C, IL-2 produced a dose dependent increase inlymphocyte migration, confirming its role in lymphocyte activation.

E. Primary Immune Response Assay 5

In a fifth example of the primary immune response assay, the effectTNF-alpha on dendritic cell migration was determined. DCs were generatedfrom peripheral blood monocytes by culture in IL-4 and GM-CSF. Induplicate wells, cells were cultured in the presence (FIG. 20, panels Band D) or absence panels A and C) of 50 ng/1 ml of TNF-alpha. Cells wereimaged every two minutes in each of the wells. The accumulated tracksfor more than 300 images are shown in FIG. 20 with light lines. Theaverage velocities for the cells over the period are plotted panel E),with error bars representing standard deviation.

As FIG. 20 shows, TNF-alpha induced cell migration of DCs. Since DCmotility is indicative of cell maturation and differentiation, the assaydemonstrates TNF-alpha's role in the DC maturation. Because mature DCsplay a central role in antigen presentation during a primary immuneresponse, the assay assists practitioners in identifying proteins activein immunopathologies.

F. Concurrent Assays

The above assays can be performed concurrently in one cell cultureplate, as shown in FIG. 21. In many cases, more than one assay can beperformed within the same well. For example, FIG. 21 demonstrates TCell—Dendritic Cell Interaction and T Cell activation occurring in asingle well. The ability to combine a variety of disease model assaysinto one cell culture plate improves throughput, productivity andsensitivity. For example, by measuring both lymphocyte speed anddirection of travel in the presence of DC, it is possible to showlymphocyte migration to specific DC for antigen presentation andsubsequent TC proliferation at that DC—all within a single well. Theassays and outputs of the previous examples A through D above can all beperformed in the single plate of FIG. 21. TABLE II Components ofAutomated Single-Cell Culture System depicted in FIG. 1. Component #Name Manufacturer Description 10 Chamber Machine Shop Parts described inTable III as components #50-92. 12 Temperature Omega Model CN76000.Input from RTD (#58 in Table Controller III); output from two heatingcartridges (#62 in Table III) 14 CO₂ Controller Omega Model CN 76000.Electrical input from sensor (#66 in Table III) mounted on Chamber(#10). Regulates internal solenoid valve which controls flow of 100% CO₂from CO₂ Supply Tank with Regulator (#16) to CO₂ Supply Fitting (#68 inTable III). 15 Temperature Omega Model CN76000. Input from RTD (#60)Controller 16 CO₂ Supply Matheson (Tank); Supplies Chamber (#10) with100% CO₂ through Tank with Regulator (Fisher) CO₂ Controller (#14).Regulator 17 Temperature Omega Model CN76000. Input from RTD (#59)Controller 18 Motorized Stage Ludl X-Y stage with 4.5″ × 3.25″ travel.Mounts on Inverted Microscope (#20); motion controlled by 2 eachMicrostepper Motor Controller Boards 73000500 and Microstepper PowerBoards 73000503 installed in Microscope Controller (#28). 20 InvertedNikon Diaphot 300, equipped with 100 white light, Microscope ELWDcondenser, 6-place nosepiece with 4x and 10x phase objectives and 20xand 40x ELWD phase objectives, HMX-4 lamphouse with Hg bulb, andepifluorescence attachment. Mounts Motorized Stage (#18), MotorizedFocus Drive Assembly (#22), High-Speed Shutter for Transmitted Light(#24), High-Speed Dual Filter Wheel with Shutter for Fluorescence (#26),and Video-Rate (#32) and Cooled (#34) CCD Cameras 22 Motorized FocusLudl Model 73000901 Focus Drive Motor Assembly Drive Assembly and Model99A006 Z-axis Control Card. Focus and Controller Drive Motor Assemblymounts on focus control of Inverted Microscope (#20) and controls focusthrough action of Control Card installed in Microscope Controller (#28).24 High-Speed Ludl Model 99A043 shutter with microscope adapter Shutterfor flange mounts on Inverted Microscope (#20). Transmitted Position ofshutter (i.e., open or close) controlled Light by Model 73000800 boardin Microscope Controller (#28). 26 High-Speed Dual Ludl Model 99A076high-speed dual 6 position filter Filter Wheel wheel with 100 msswitching between filters and with Shutter for high-speed shutter forexcitation by Fluorescence epifluorescence. Position of filter wheel andshutter controlled by Model 73000800 board in Microscope Controller(#28). 28 Microscope Ludl Model 990082 19″ automation electronicsController console with joystick. Controls movement of Motorized Stage(#18) and Motorized Focus Drive Assembly (#22) and position of High-Speed Shutter for Transmitted Light (#24) and High-Speed Dual FilterWheel with Shutter for Fluorescence (#26) through communications withQuadra 950 (#42) by RS-232 interface. 30 Joystick Ludl Model 73000362.X-Y action controls sets initial position of Motorized Stage (#18);Z-axis digipot set initial position of Motorized Focus Drive Assembly(#22). 32 Cooled CCD Photometrics High performance cooled CCD camerawith Camera Kodak Model KAF1400 Grads 1 chip with 1317 × 1035 pixelresolution, and 12-bit/pixel gray scale resolution at 500 kHz and CE200ACamera Electronics Unit controller. Output to PC (#42). 38 Imaging BoardPhotometrics Photometrics, PCI interface board for KAF 1400 camera. 42Pentium III PC Gateway Pentium III PC with 256 MB RAM, 20GB harddisk,connected to a Cooled CCD Camera. 44 Video Board Gateway, IncAccelGraphics Permedia 2 AGP 8 MB Video Card 46 Computer Gateway 17″Multiscan color monitor. Input from PC Monitor 66 CO2 Sensor ValtronicsValtronics, 3463 Double Springs Road, Valley Springs CA 95252 model2007DHH-R, 0-10% CO2 68 Supply Fitting McMaster-Carr McMaster-Carr Part# 52065K113 ⅛ T × ⅛ NPT 72 Quick McMaster-Carr McMaster-Carr Part #52065K 151 ⅛ T × ⅛ T Disconnect Fitting 108 Syringe Kloehn Ltd KloehnPart # 50300 114 Distribution Kloehn Ltd Kloehn Part # 50120 Valve

TABLE III Components of Chamber for Automated Single-Cell Culture System(Component #10 in FIG. 1 and Table II) Component # Name Description 50Chamber Body Constructed of anodized aluminum. Forms enclosed chamber(#10 in Table II) by assembly with Chamber Cover (#52) and TurbineHousing (#76). Mounts screwed in Thermocouple Fitting (#60) withThermocouple (#58), 2 Heating Cartridges (#62) secured with HeatingCartridge Retaining Screws (#64), CO₂ Sensor (#66) by two 1½″ × 3/16″hex-nut headed screws, screwed-in CO₂ Supply Fitting (#68), screwed-inPressure Relief Fitting (#70), and 3 screwed in Unused Port Plugs (#74).Gas-tight seal between Chamber Body and Chamber Cover (#52) maintainedby tightening 8 0.50″ × 0.19″ hex-nut headed screws with Chamber CoverGasket (#56) in place; gas tight seal between Chamber Body and TurbineHousing maintained by tightening two 1¼″ × 3/16″ hex-nut headed screwswith Turbine Housing O-Ring (#86) in place. 52 Chamber Cover Constructedof anodized aluminum. Glass Observation Window (#54) glued with siliconerubber into inset. Mounted on top of Chamber Body (#50) of chamber by 80.50″ × 0.19″ hex-nut headed screws. Gas tight seal between Chamber Bodyand Chamber Cover maintained by tightening screws with Chamber CoverGasket (#56) in place. 54 Glass One each 5.00″ × 3.41″ × 0.01″optical-grade glass slides glued by silicone Observation rubber intoinset on bottom of Chamber Body (#50) and inset on top of Windows (2)Chamber Cover (#52). 56 Chamber Cover Silicone rubber O-ring gasket(size #162) forms gas-tight seal between Gasket Chamber Body (#50) andChamber Cover (#52) with tightening of 8 0.50″ × 0.19″ hex-nut headedscrews. Outer dimensions 6.30″ × 4.33″, inner dimensions 5.25″ × 3.50″,thickness 0.01″. 58 RTD (Resistance RDF Corp Part # 29228-Tol-B-24Temperature Device) 62 Heating 20 watt McMaster-Carr heating cartridge.Each mounts into ports on front of Cartridges (2) Chamber Body (#50) andsecured in place by a Heating Cartridge Retaining Screw (#64). Eachconnected by insulated electrical wire to Temperature Controller (#12 inTable II). 64 Heating One each secures on Heating Cartridge (#62) insidewalls of Chamber Body Cartridge (#50) through ports on front ofChamber Body. Constructed of anodized Retaining Screws aluminum. Mountby screwing into Chamber Body. (2) 68 CO₂ Supply Teflon elbow, ⅛ NPT,screwed and sealed with teflon tape into front port on Fitting ChamberBody (#50). Connected by Tygon tubing to CO₂ Controller (#14 in TableII). 74 Unused Port Stainless steel fittings with threads wrapped inTeflon tape and screwed into Plugs (3) unused ports of Chamber Body(#50). 90 House Air Teflon elbow, ⅛ NPT, screwed and sealed with teflontape into side port on Fittings (2) Turbine Housing (#76). Connected byTygon tubing to House air supply. 230 Advanced Liquid Advanced LiquidHandling model MBP 2000 (Williams Bay, WI) Handling

1. A protein-analysis method comprising: (A) bringing a protein intocontact with at least a first disease-model cell and a seconddisease-model cell, respectively, wherein each of said first and secondcells is located in a separate well; then (B) determining the dynamicstate of each of said cells, whereby a data set is generated for eachcell; and (C) analyzing the data set for said first cell and the dataset for said second cell, to obtain information about the function ofsaid protein.
 2. A method according to claim 1, wherein said firstdisease-model cell and said second disease-model cell relate to the samedisease model.
 3. A method according claim 1, wherein the data sets ofstep (C) address a plurality of cell parameters.
 4. A method accordingto claim 1, wherein said determination of the dynamic state comprisesimaging each of said cells by either visible or fluorescent light, orboth.
 5. A method according to claim 1, wherein step (A) comprisesbringing said protein into contact with a first plurality of firstdisease-model cells and a second plurality of second disease-modelcells, respectively, and wherein said information distinguishes asubpopulation of at least one of said first and second pluralities.
 6. Amethod according to claim 1, further comprising providing a plurality ofproteins, wherein step (A) comprises bringing into contact, with Nnumber of disease-model cells, a chosen protein from said plurality suchthat each of at least some of said N cells contacts a different proteinfrom said plurality, N being an integer greater than
 2. 7. A methodaccording to claim 6, wherein more than one well receives the sameprotein from said plurality of proteins.
 8. A method according to claim6, wherein at least one well does not receive a protein from saidplurality of proteins.
 9. A method according to claim 6, wherein atleast one well receives more than one protein from said plurality ofproteins.
 10. A method according to claim 6, wherein the data sets ofstep (C) address a plurality of M cell parameters, M being an integer of1 or greater.
 11. A method according to claim 10, wherein said cellparameters comprise two or more of the measured parameters enumerated inTable I.
 12. A method according to claim 10, wherein a data set of step(C) is organized as an N×M array of values.
 13. A method according toclaim 10, wherein either said first disease-model cell or said seconddisease-model cell employs an oncogenesis disease model.
 14. A methodaccording to claim 10, wherein either said first disease-model cell orsaid second disease-model cell employs a primary immune response diseasemodel.
 15. A method according to claim 10, wherein either said firstdisease-model cell or said second disease-model cell employs anangiogenesis disease model.
 16. An integral array of biochambers, each(i) comprising a well in which a disease-model cell is located and (ii)defining a separate, closed environment for said cell, wherein each wellcontains a protein and said array presents a predetermined pattern ofassociation between wells and proteins.
 17. A protein-analysis methodcomprising: (A) disposing a first disease-model cell in a first well ina manner wherein at least one cell is individually observable; (B)disposing a second disease-model cell in a second well in a mannerwherein at least one cell is individually observable; (C) bringing aprotein into contact with said first and second disease-model cells; (D)repeatedly observing the first and second disease-model cells; (E)compiling data in the form of data sets pertaining to a change in atleast one of a plurality of observable characteristics of each of therespective first and second disease-model cells, prior to and subsequentto the protein being contacted with the first and second disease-modelcells; and (F) analyzing the data sets to obtain information about thefunction of the protein.
 18. A method according to claim 17, whereinsaid first disease-model cell and said second disease-model cell relateto the same disease model.
 19. A method according to claim 17, furthercomprising adding a modifying agent.
 20. A method according to claim 17,wherein steps (A) through (D) are implemented robotically, within aclosed environment.
 21. A method according to claim 17, wherein steps(A) through (F) are implemented robotically.
 22. A method according toclaim 17, wherein the step of repeatedly observing is carried outoptically.
 23. A method according to claim 17, wherein the observablecharacteristics are selected from the group consisting of cell movement,cell division, apoptosis, morphology, adherence and physiologicalfunction
 24. A method according to claim 17, wherein the observablecharacteristics comprise the measured parameters enumerated in Table I.25. A method according to claim 17, further comprising means forselectively adding a modifying agent in addition to the protein.
 26. Aprotein-analysis apparatus comprising: means for disposing a pluralityof first disease-model cells in a first well in a manner wherein atleast one of the first disease-model cells is individually observable;means for disposing a plurality of second disease-model cells in asecond well a manner wherein at least one of the second disease-modelcells is individually observable; means for bringing a protein intocontact with at least one of the first and second disease-model cells;means for repeatedly observing the first and second disease-model cells;means for compiling and analyzing data in the form of data sets thatpertain to a change in at least one of a plurality of observablecharacteristics of each of the respective first and second disease-modelcells, prior to and subsequent to the protein being contacted with thefirst and second disease-model cells.
 27. A protein-analysis methodcomprising: (A) disposing a disease-model cell in a well in a mannerwherein at least one cell is individually observable; (B) bringing aplurality of proteins into contact with said disease-model cell; (D)repeatedly observing said disease-model cell; (E) compiling data in theform of data sets pertaining to a change in at least one of a pluralityof observable characteristics of disease-model cell, prior to andsubsequent to the proteins being contacted with the disease-model cell;and (F) analyzing the data sets to obtain information about the functionof the proteins.
 28. The method of claim 27, further comprisingisolating a protein of interest by splitting said plurality into asmaller number of pluralites and repeating steps (A) thru (F), usingsaid smaller number of pluralites for step (13).
 29. The method of claim27, further comprising isolating a protein of interest by splitting saidplurality into individual proteins and repeating steps (A) thru (F) foreach of said proteins.